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陆军军医大学(第三军医大学)第一附属医院皮肤科,重庆
葛兰,E-mail: 43793340@qq.com
翟志芳,E-mail: zhaizf1004@tmmu.edu.cn
收稿日期:2025-02-16,
修回日期:2025-03-03,
纸质出版日期:2025-04-15
移动端阅览
孙露,李想,王金秋,等.基于生信分析玫瑰痤疮毛细血管增生相关关键基因的筛选[J].陆军军医大学学报,2025,47(07):701-707.
SUN Lu,LI Xiang,WANG Jinqiu,et al.Screening of key genes related to angiogenesis in rosacea based on bioinformatics analysis[J].J Army Med Univ,2025,47(07):701-707.
孙露,李想,王金秋,等.基于生信分析玫瑰痤疮毛细血管增生相关关键基因的筛选[J].陆军军医大学学报,2025,47(07):701-707. DOI:10.16016/j.2097-0927.202502047.
SUN Lu,LI Xiang,WANG Jinqiu,et al.Screening of key genes related to angiogenesis in rosacea based on bioinformatics analysis[J].J Army Med Univ,2025,47(07):701-707. DOI:10.16016/j.2097-0927.202502047.
目的
2
利用生物信息学方法分析玫瑰痤疮(rosacea,RA)毛细血管增生相关差异表达基因,筛选关键基因并验证其在RA中的表达水平。
方法
2
从基因表达综合数据库(Gene Expression Omnibus,GEO)获取基因芯片数据集GSE65914,应用R语言分析得到与RA相关的差异表达基因,并与GeneCards数据库中的血管增生相关基因取交集,获得RA血管增生相关基因,进一步基于蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络分析及Cytoscape算法筛选关键基因。建立RA小鼠模型,通过RT-qPCR验证以上关键基因在RA皮损组织中的mRNA表达水平。
结果
2
从GEO中分析获得947个RA相关差异表达基因,进一步获得202个RA血管增生相关基因,最终筛选出具有显著差异的三个关键基因
CXCL8
、
IL-1B
、
STAT1
。RT-qPCR结果表明,
MIP-2
、
GCP-2
、
IL-1B
、
STAT1
在RA皮损中的mRNA表达水平显著高于正常对照(
P
<
0.05)。
结论
2
本研究通过生物信息学方法筛选并验证了RA血管增生的关键基因
CXCL8
、
IL-1B
和
STAT1
,为揭示RA血管增生可能机制及研究靶向治疗方案提供了理论依据。
Objective To investigate the differential expression genes (DEGs) related to angiogenesis in rosacea (RA) by utilizing bioinformatics analysis in order to screen the key genes and verify their mRNA expression levels. Methods The gene microarray dataset GSE65914 was retrieved from the Gene Expression Omnibus (GEO) repository. Analyzed by R programming
the dataset was refined to identify DEGs related to RA
and then cross-referenced with angiogenesis-related genes from the GeneCards database to get a subset specific to RA angiogenesis. The process of identifying key genes was augmented by employing protein-protein interaction (PPI) network analysis and Cytoscape-based computational algorithms. The mRNA expression levels of the aforementioned pivotal genes were detected by real-time fluorescent quantitative reverse transcription PCR (RT-qPCR). Results A total of 947 RA-associated DEGs were identified from GEO dataset
and then 202 genes related to RA angiogenesis were further delineated. PPI network analysis and Cytoscape algorithm finally identified 3 key genes
that is
CXCL8
IL-1B
and
STAT1
. The results of RT-qPCR showed that the mRNA expression levels of
MIP-2
GCP-2
IL-1B
and
STAT1
in RA lesions
were significantly higher than those in normal controls (
P
<
0.05). Conclusion With aid of bioinformatics analysis
our study has screened and validated key genes associated with angiogenesis in RA
namely
CXCL8
IL-1B
and
STAT1
which providing a theoretical basis for elucidating the potential mechanisms underlying RA-induced angiogenesis and developing targeted therapeutic strategies.
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